Changes in Cost-Effectiveness for Chronic Hepatitis C Virus Pharmacotherapy: The Case for Continuous Cost-Effectiveness Analyses

BACKGROUND: Cost-effectiveness evaluations for hepatitis C virus (HCV) treatments have been published frequently, but new products with significant cost and effectiveness differences make these analyses obsolete. How valuable are economic models for a fixed time period in a dynamic market? OBJECTIVE: To estimate the cost-effectiveness of the best available HCV treatment at different points in time, using the same comparator to demonstrate how rapid innovation in a disease area influences economic outcomes. METHODS: A Markov model was used to calculate the cost-effectiveness of treatment in 2010, 2012, 2014, 2016, and 2018 compared with a standard comparator (no treatment) from the payer perspective. Expected drug costs and treatment effectiveness estimates for sustained virologic response (SVR) were calculated using recommended regimens for each of the 6 HCV genotypes at each time point and distribution of genotypes in the United States. Patients entered the model with different stages of fibrosis. Utility estimates for each health state were used to calculate quality-adjusted life-years (QALYs) earned at each cycle. Incremental cost-effectiveness ratios were reported for each year to compare the “treatment versus no treatment” decision at that time. RESULTS: No HCV treatment resulted in a gain of 11.54 QALYs over a 20-year time horizon at a cost of $42,938. Costs for treated groups were $69,075, $123,267, $125,431, $86,782, and $56,470 for the 2010, 2012, 2014, 2016, and 2018 scenarios, respectively. QALYs gained for treated groups were 12.90, 12.97, 13.34, 13.39, and 13.46 for the 2010, 2012, 2014, 2016, and 2018 scenarios, respectively. The incremental cost-effectiveness ratios in each year compared with no treatment were $19,218 per QALY, $56,104 per QALY, $45,829 per QALY, $23,699 per QALY, and $7,048 per QALY. CONCLUSIONS: Treatment effectiveness for HCV has increased steadily, while treatment costs increased substantially from 2010-2014 before decreasing to its lowest point in 2018. Thus, the dynamic nature of innovation creates the need for iterative cost-effectiveness analyses.

T he last decade of advances in the treatment for hepatitis C virus (HCV) has provided an interesting case study of value assessment that pairs significant health gains with headline-grabbing costs. In 2011, the first 2 entities, boceprevir and telaprevir, of a new class of direct-acting antivirals (DAAs) entered the market, offering increased cure rates over the existing treatments of peginterferon/ribavirinonly regimens. 1 In 2013, the announcement of sofosbuvir's approval with even higher cure rates and a "$1,000 a pill" list price sparked new debate among many stakeholders and provided increased demand for health economic evaluations. 2,3 Sofosbuvir's approval was a classic example of a substantial investment in pharmaceutical costs today compared with future cost savings expected over a cured patient's lifetime. With the evidence available for liver fibrosis progression, this comparative research question was great for modeling using a health-state approach, and the publication of several economic models ensued. 3,4 Finally, in 2015 the Institute for Clinical and Economic Review (ICER) published a value assessment of newly approved DAAs and concluded that, despite the new regimens being cost-effective at generally accepted thresholds, 10 out of 12 panelists voted that DAAs represent an overall low value to a state Medicaid program because of the potential budget impact. 5 Since the development and publication of ICER's DAA value assessment, 3 more DAAs entered the market with significantly lower list prices and additional gains in terms of viral genotype coverage. [6][7][8] Additionally, multiple DAAs have been voluntarily withdrawn from the U.S. market by their manufacturers. The clinical effectiveness of newer products and market dynamics have reshaped the clinical guidelines substantially for HCV. 9 With lower drug prices and increased effectiveness, the pharmacoeconomics of HCV treatment have rapidly changed in a • With lower drug prices and increased effectiveness, the pharmacoeconomics of hepatitis C virus (HCV) treatment have rapidly changed in a very short time span, with several new products and multiple products withdrawn. • Cost-effectiveness analyses (CEAs) for any treatment, including HCV, are typically fixed to answer a single question to compare treatments available at the time the study is conducted.

What is already known about this subject
• Dynamic markets with rapid innovation in a short time period limit the usefulness of any single published CEA. • In a short period of time, hepatitis C treatment has evolved dramatically with improvements in treatment effectiveness and concurrent declines in treatment costs. This study aimed to model the cost-effectiveness of DAAs across multiple time points to demonstrate the effect of innovation and competition in a rapidly changing environment. Previous research of cost-effectiveness analyses (CEAs) for chronic HCV have reported common practices in the design of these models and the importance of input parameters, such as rate of sustained virologic response (SVR), cost of DAAs, and comparator selection. 3,10 This study focuses on these 3 items and discusses the challenges for CEA developers that must be addressed to improve the usefulness of their results.    15 Major gains in effectiveness followed with the approval of sofosbuvir in late 2013, which had an effect on the 2014 standard of care for genotypes 1, 2, and 4. 15,16 All-oral regimens continued to improve the effectiveness of the standard of care for genotypes 1 and 4 in 2016 (elbasvir/ grazoprevir), culminating with highly effective pangenotypic agents by 2016-2017 (i.e., sofosbuvir/velpatasvir and glecaprevir/pibrentasvir). 9,16 Patients entered the model with different stages of liver fibrosis based on U.S. estimates. 4,12,17 Natural disease progression, post-SVR progression, and advanced fibrosis progression for reinfected patients were incorporated into the model. 11,18 Probability of treatment success for each time point was a function of the reported treatment effectiveness and population distribution for each genotype. 9,14-16 For example, cure rates ranged from 30%-65% in 2010 based on genotype. The probability of treatment success for each genotype was multiplied by the prevalence of that genotype, and the sum of the resulting product for all 6 genotypes determined the overall probability of treatment success (Equation 1). All model transition probabilities are provided in Table 1.  Health-state cost estimates from the 2015 ICER value assessment were applied for all time points but were adjusted to 2018 U.S. dollars for direct comparison. 5 These costs only reflect direct medical costs associated with each health state (Table 1).

Utilities
For consistency, the same utility estimates for each health state reported in the 2015 ICER report were used to calculate quality-adjusted life-years (QALYs) earned at each cycle. 5

Analysis
The primary analysis for this methodology study focused on the changing costs and effectiveness estimates at each time point to estimate incremental cost-effectiveness ratios. A scenario analysis was conducted using only the WAC for each drug referenced in RED BOOK to describe the effect of using list versus net price in the CEA. 21

Scenario Analysis
When using WAC as the primary drug price input, the 2018 HCV treatment option was still preferred to other scenarios (Table 3). Depending on the decision maker's willingness-topay threshold ($50,000/QALY, $100,000/QALY, and $150,000/ QALY), the final determination of cost-effectiveness may have changed. While all treatments would have been costeffective compared with no treatment at a $100,000 per QALY threshold, a $50,000 per QALY threshold would have resulted in 2012 and 2014 treatment options being considered not costeffective compared with no treatment.

■■ Discussion Dynamic Cost-Effectiveness in 1 Model
Cost-effectiveness researchers have great influence over the outcome of an economic model through every methodological decision made from the project initiation to completion. The recommendations of expert panels on cost-effectiveness in 1996 and 2016 meant to improve the quality of CEAs and promote comparability across studies have helped guide these methodological decisions but are not capable of solving a major challenge in the implementation of cost-effectiveness research: things change. 22,23 The evolution of HCV treatment options over the past decade has generated a valuable case study for those interested in advancing the methods of CEA and organizations conducting value assessments to guide public and private payer decisions. The research and effort required to conduct a thorough CEA that meets best-practice recommendations involves substantial investment and typically ends with a published report or manuscript that has survived the scrutiny of the peer-review process. Once the final CEA has been delivered to the intended audience, the research team moves on to the next project and the cycle continues. The frequency at which these CEAs should be updated with new inputs and assumptions may be limited by resources. However, the case of HCV demonstrates that the value assessment for treatment in rapidly changing clinical scenarios may require revisions every few years.

Cost-Effectiveness of Each Treatment Scenario in Order of Ascending Effectiveness, Excluding Dominated Options
Changes in Cost-Effectiveness for Chronic Hepatitis C Virus Pharmacotherapy: The Case for Continuous Cost-Effectiveness Analyses innovation. The recent use of subscription-based payment models using a multiyear fixed-price contract for HCV treatments may be an area where more frequent CEA could be needed. 25 A multiyear fixed-price contract between a payer and manufacturer initiated in 2012 or 2014 might have been more costly than the proposed state-level subscriptions recently announced. 26

The Role of Model Transparency
Published CEAs that are fully transparent with detailed technical appendices could encourage more frequent updates and revisions. The Professional Society for Health Economics and Outcomes Research and Society for Medical Decision Making (ISPOR-SMDM) joint task force guidelines on model transparency and validation define transparency as "clearly describing the model structure, equations, parameter values, and assumptions to enable interested parties to understand the model." 27 While transparency could serve as a step along the path to more frequent updates, transparency should not be confused with accuracy or validity. 27,28 In order to feel more confident with model results, researchers should consider inclusion of a multistakeholder advisory board to assist with face validity of the model structure and assumptions and compare model results to similar published models for cross validation. 11,27,29 Similar to a 2016 systematic review of cost-effectiveness studies of DAAs, we found that DAA prices used and SVR rates had an influence over the resulting incremental costeffectiveness ratio. 3 However, a payer cost-effectiveness threshold would be needed to determine whether the final decision recommendation would be to cover or not to cover. For example, a $100,000 per QALY threshold applied to our model may result in the same coverage determination. Additionally, this CEA example does not address the potential changing population that a payer may have to account for. In the case of a curable infectious disease, a dynamic modeling approach may be necessary to incorporate other benefits of treatment (e.g., role of treatment in preventing new cases) on a covered population. 3

Decision Making
Budgeting processes typically follow a fiscal calendar where decisions are made annually, so updated CEA results could influence formulary decisions from year to year. While this could affect many countries with government-supported health technology assessment bodies, the uptake of CEA evidence into formulary decisions in the United States has been less clear. 24 While this model demonstrates the variation in CEA results over time, one potential interpretation could be increased value with

Resources Needed
Even if the same model structure and methods are used, significant effort would be required in the evidence synthesis process and in ensuring that all parameter values are updated appropriately. Additionally, the personnel needed to replicate and update current models may require advanced training and could be in short supply. The use of open-source programs and code sharing could reduce costs to some degree, but technical support to ensure that coding errors are not introduced may not be completely eliminated. Having an organization financially supported and charged with this task may help build efficiency, but there may be other unintended consequences of this charge falling on only this one group.

Limitations
This analysis includes limitations that readers should consider while interpreting the results. First, the health-state costs were kept constant for comparison between treatment arms. We justified this decision based on the focus of the analysis, but if hospital, laboratory, clinic visits, or surgical costs also change dramatically then it would be appropriate to adjust. Using a VA drug cost for our reference case also limits the analysis for commercial payers or plans that do not receive the same level of rebates on DAA costs as the VA. While we conducted a scenario analysis using WAC, commercial plans may experience prices that fall somewhere in between the VA and WAC. 20 Finally, reinfection rates or other dynamic population considerations were not included in this model, since we determined that these variables would be consistent across the time periods and treatments.

■■ Conclusions
Treatment effectiveness for HCV has increased steadily, while treatment costs increased substantially from 2010-2014 before decreasing to its lowest point in 2018. The dynamic nature of CEAs in a disease state with rapid pharmaceutical innovation may cause some concern for decision makers who rely on a single analysis over time. Model transparency along with resources to update or revise model assumptions would enable